Multi-agent collaboration brings human-like reasoning to machine translation
Three AI agents working together crack the code of literary translation
DRT-o1 enhances machine translation by incorporating long chain-of-thought reasoning, particularly for translating literary texts with complex metaphors and similes between languages.
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https://arxiv.org/abs/2412.17498
🤔 Original Problem:
Traditional machine translation struggles with literary texts containing metaphors and similes due to cultural differences, where literal translations often fail to capture intended meanings.
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🔧 Solution in this Paper:
→ The paper introduces DRT-o1, a multi-agent framework using three specialized agents: translator, advisor, and evaluator
→ The translator iteratively refines translations based on advisor suggestions and evaluator scores
→ They mine literature sentences containing similes/metaphors from Project Gutenberg books
→ GPT-4o reformulates the translations to enhance readability and fluency
→ The system is trained on Qwen2.5 and LLama-3.1 backbones
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💡 Key Insights:
→ Not all translation scenarios require long thought processing
→ Literary translation benefits significantly from chain-of-thought reasoning
→ Multi-agent collaboration produces better translations than single-model approaches
→ The system requires longer inference time compared to vanilla models
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📊 Results:
→ DRT-o1-14B outperforms Qwen2.5-14B-Instruct by 2.45 GRF, 0.1 CometKiwi, and 6.23 BLEU scores
→ Achieves 87.19 GRF score compared to QwQ-32B-preview's 86.31
→ Shows consistent improvement across all evaluation metrics
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